Not everyone ages in the same manner. It is well known that women tend to live longer than men, and lifestyle choices such as smoking and physical fitness can hasten or delay the aging process (Steven N., 2006; Blair et al., 1989). These observations have led to the search for molecular markers of age which can be used to predict, monitor, and provide insight into age-associated physiological decline and disease. One such marker is telomere length, a molecular trait strongly correlated with age (Harley et al., 1990) which has been shown to have an accelerated rate of decay under environmental stress (Epel et al., 2004; Valdes et al.). Another marker is gene expression, especially for genes that function in metabolic and DNA repair pathways which are predictive of age across a range of different tissue types and organisms (Fraser et al., 2005; Zahn et al., 2007; de Magalhães et al., 2009).
A growing body of research has reported associations between age and the state of the epigenome—the set of modifications to DNA other than changes in the primary nucleotide sequence (Fraga and Esteller, 2007). In particular, DNA methylation associates with chronological age over long time scales (Alisch et al., 2012; Christensen et al., 2009; Bollati et al., 2009; Boks et al., 2009; Rakyan et al., 2010; Bocklandt et al., 2011; Bell et al., 2012) and changes in methylation have been linked to complex age-associated diseases such as metabolic disease (Barres and Zierath, 2011) and cancer (Jones and Laird, 1999; Esteller, 2008). Studies have also observed a phenomenon dubbed “epigenetic drift”, whereby the DNA methylation marks in identical twins increasingly differ as a function of age (Fraga et al., 2005; Boks et al., 2009). Thus, the idea of the epigenome as a fixed imprint is giving way to the model of the epigenome as a dynamic landscape that reflects a variety of chronological changes. The current challenge is to determine whether these changes can be systematically described and modeled to detect different rates of human aging, and to tie these rates to related clinical or environmental variables.
The mechanisms that drive changes in the aging methylome are not well understood, although they have been attributed to at least two underlying factors (Vijg and Campisi, 2008; Fraga et al., 2005). First, it is possible that environmental exposure will over time activate cellular programs associated with consistent and predictable changes in the epigenome. For example, stress has been shown to alter gene expression patterns through specific changes in DNA methylation (Murgatroyd et al., 2009). Alternatively, spontaneous epigenetic changes may occur with or without environmental stress, leading to fundamentally unpredictable differences in the epigenome between aging individuals. Spontaneous changes may be caused by chemical agents that disrupt DNA methyl groups or through errors in copying methylation states during DNA replication. Both mechanisms lead to differences between the methylomes of aging individuals, suggesting that quantitative measurements of methylome states may identify factors involved with slowed or accelerated rates of aging.
To better understand how the methylome ages and to determine whether human aging rates can be quantified and compared, we initiated a project to perform genome-wide methylomic profiling of a large cohort of individuals spanning a wide age range. Based on these findings, we constructed a predictive model of aging rate which we show is influenced by gender and specific genetic variants. These data help explain epigenetic drift and suggest that age-associated changes in the methylome lead to changes in transcriptional patterns over time. These findings were replicated in a second large cohort.
The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we have created a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages. Furthermore, we have discovered that differences in aging rates may explain epigenetic drift and are reflected in the transcriptome. Our discovery highlights specific components of the aging process and provides forensic methods, screening methods for agents retarding or accelerating aging, and methods for preventing and treating diseases.